Supplementary Material for: A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model

Objective: The existing methods for identifying multiple rare variants underlying complex diseases in family samples are underpowered. Therefore, we aim to develop a new set-based method for an association study of dichotomous traits in family samples. Methods: We introduce a framework for testing the association of genetic variants with diseases in family samples based on a generalized linear mixed model. Our proposed method is based on a kernel machine regression and can be viewed as an extension of the sequence kernel association test (SKAT and famSKAT) for application to family data with dichotomous traits (F-SKAT). Results: Our simulation studies show that the original SKAT has inflated type I error rates when applied directly to family data. By contrast, our proposed F-SKAT has the correct type I error rate. Furthermore, in all of the considered scenarios, F-SKAT, which uses all family data, has higher power than both SKAT, which uses only unrelated individuals from the family data, and another method, which uses all family data. Conclusion: We propose a set-based association test that can be used to analyze family data with dichotomous phenotypes while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships.研究目标：当前用于在家族样本中鉴定复杂疾病潜在多罕见变异的方法检验效能不足。为此，本研究拟开发一种全新的基于集合的关联分析方法，用于家族样本中二分类性状的关联分析。 研究方法：本研究提出一种基于广义线性混合模型（generalized linear mixed model）的家族样本遗传变异与疾病关联检验框架。所提方法依托核机器回归（kernel machine regression），可视为序列核关联检验（sequence kernel association test, SKAT 与 famSKAT）针对携带二分类性状的家族数据的扩展版本，命名为F-SKAT。 研究结果：本研究的模拟实验显示，直接将原始SKAT应用于家族数据时，会出现I类错误率膨胀的问题；与之相比，我们提出的F-SKAT能够保持正确的I类错误率。此外，在所有考察的研究场景中，利用全部家族数据的F-SKAT，其检验效能均优于仅使用家族样本中无关个体的SKAT，以及另一种同样利用全部家族数据的关联检验方法。 研究结论：本研究提出了一种基于集合的关联检验方法，可用于分析带有二分类表型的家族数据，同时能够处理效应方向相同或相反的遗传变异，以及任意类型的家族亲缘关系。